Dynamic Search Engine Query Language Extension
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Solution Overview
Problem
Existing systems for searching structured documents face challenges in automatically updating search engines and query formulation user interfaces with new annotation types, leading to inefficiencies and inaccuracies, especially in dynamic document corpora where annotation types evolve over time.
Innovation Solution
A system that automatically updates its search engine and query formulation user interface with new annotation types by integrating a natural language processing framework to generate and insert annotations, and dynamically updates the query language definition, enabling immediate processing of structured queries referencing new annotation types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates are used to add new annotation types to the search engine, then system reliability is maintained through controlled updates, but productivity decreases due to time-consuming manual intervention
Solution Approach 1:
The system performs self-updates by automatically detecting new annotation types in the document corpus and registering them with the search engine without requiring manual intervention. The search engine monitors the corpus dynamically and adapts its query language definitions autonomously when new annotation patterns are encountered.
Solution Approach 2:
The search engine transitions from a static configuration to a dynamic system that continuously adapts to new annotation types. The query language definition is updated in real-time as new annotations are added to the corpus, allowing the system to evolve with the document collection without manual reconfiguration.
2Adaptability or versatility
If the search engine is updated to support new annotation types, then adaptability improves, but device complexity increases due to additional configuration requirements
Solution Approach 1:
The system implements a feedback mechanism where the search engine continuously monitors the document corpus for new annotation types. When new annotations are detected, the system automatically feeds this information back to update the query language definition, creating a closed-loop adaptation process that reduces configuration complexity.
Solution Approach 2:
The search engine is designed with universal capabilities to handle any annotation type that may appear in the corpus. Rather than configuring specific annotation types in advance, the system develops a universal query processing framework that can accommodate diverse annotation patterns through automatic type inference and registration.
3Measurement precision
If structured searches are performed on dynamic corpora, then measurement precision improves for finding relevant information, but loss of time occurs due to the need to update search capabilities continuously
Solution Approach 1:
The search engine maintains continuous operation while adapting to new annotation types. Rather than stopping to update configuration files or requiring system downtime, the engine processes search queries and incorporates new annotation types simultaneously, ensuring uninterrupted search functionality with improved precision over time.
Solution Approach 2:
The system performs preliminary registration of new annotation types in the background before they are needed for searches. By proactively detecting and registering new annotation patterns as they appear in the corpus, the system ensures search accuracy is maintained without requiring time-consuming updates when searches are actually executed.
Data Source
AI summary
A system includes a document corpus containing structured documents, which contain both text and annotations of the text. The system also includes a search engine which is adapted to perform structured searches of the structured documents. As new types of annotations are added to the system, the search engine is updated automatically to become capable of performing structured searches for the new types of annotations. For example, if a new natural language processing (NLP) component, adapted to generate annotations of a new type, is added to the system, then the system automatically updates a query language to include a definition of the new type of annotation. The search engine may then immediately be capable of processing structured queries which refer to the new type of annotation.